Robust regression and outlier detection
Robust regression and outlier detection
Introduction to algorithms
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the goal of sensory coding?
Neural Computation
A sparse representation for function approximation
Neural Computation
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Recognition with Occlusions
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Sparse Representations for Image Decomposition with Occlusions
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
FRAME: Filters, Random fields, and Minimax Entropy-- Towards a Unified Theory for Texture Modeling
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Eigenfeatures for planar pose measurement of partially occluded objects
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Dealing with occlusions in the eigenspace approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Numerical Methods
Point process models for event-based speech recognition
Speech Communication
Fast moment estimation in data streams in optimal space
Proceedings of the forty-third annual ACM symposium on Theory of computing
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We are given an image I and a library of templates {\cal L}, such that {\cal L} is an overcomplete basis for I. The templates can represent objects, faces, features, analytical functions, or be single pixel templates (canonical templates). There are infinitely many ways to decompose I as a linear combination of the library templates. Each decomposition defines a representation for the image I, given {\cal L}.What is an optimal representation for I given {\cal L} and how to select it? We are motivated to select a sparse/compact representation for I, and to account for occlusions and noise in the image. We present a concave cost function criterion on the linear decomposition coefficients that satisfies our requirements. More specifically, we study a “weighted L norm” with 0